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研究生:簡暐晉
研究生(外文):Wei-Chin Chien
論文名稱:基於解構式生成對抗網路於多變數工業傳感資料之異常檢測
論文名稱(外文):Anomaly Detection for Multivariate Industrial Sensor Data via Decoupled Generative Adversarial Network
指導教授:王勝德王勝德引用關係
指導教授(外文):Seng-De Wang
口試委員:雷欽隆余承叡
口試委員(外文):Chin-Laung LeiCheng-Juei Yu
口試日期:2022-02-08
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:電機工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2022
畢業學年度:110
語文別:英文
論文頁數:32
中文關鍵詞:異常偵測時間序列自編碼器生成對抗網路
外文關鍵詞:Anomaly DetectionTime SeriesAutoencoderGenerative Adversarial Network
DOI:10.6342/NTU202200380
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在工業控制系統中,時常產生具有時間序列關係的數據。這些數據可能來自於橋樑震動,配水系統,或製造設備的監測數據。本文提出一個可預測多變數時間序列的異常偵測模型與訓練方法。該方法基於自編碼器,並且近一步使用生成式對抗網路,結合由生成模型產生殘差,以及判别模型輔助產生的異常分數,以增加預測精準度。所提出的演算法也降低了訓練生成式對抗網路的難度,並且該方法在SWaT, BATADAL, 以及Rare Event Classification 資料集上均比常見方法在F1 socre上取得了更好的表現。
Industrial control systems often contain sensor and actuator devices, which provide monitoring data in the form of time series, such as bridge vibrations, water distribution systems, and human physiological data. This thesis proposes an anomaly detection model based on an autoencoder that can consider time-series relations of the data. Moreover, the quality of the decoder output is further improved by adding a residual produced by an extra generator and discriminator. The proposed autoencoder-GAN model and detection algorithm not only improved the performance but also made the training process of GAN easier. The proposed deep learning model with the anomaly detection algorithm has been shown to achieve better results on the SWaT, BATADAL, and Rare Event Classification datasets over common methods.
Contents
Acknowledgements i
摘要 iii
Abstract v
Contents vii
List of Figures ix
List of Tables xi
Chapter 1 Introduction 1
Chapter 2 Related Work 5
2.1 Traditional Method........................... 5
2.2 DeepLearningMethod......................... 6
Chapter 3 Proposed Approach 11
3.1 Overview................................ 11
3.2 Problem formulation .......................... 11
3.3 Stage1:Autoencoder.......................... 12
3.4 Stage2:GAN.............................. 14
3.5 Anomaly Detection-Testing Stage ................... 16
Chapter 4 Experiments 19
4.1 Setup ................... 19
4.1.1 Dataset................................. 19
4.1.2 SWaT(SecureWaterTreatment) ................... 19
4.1.3 BATADAL (BATtle of the Attack Detection ALgorithms) . . . . . . 20
4.1.4 RareEventClassification....................... 20
4.2 Evaluation metrics ........................... 21
4.3 Baseline Models for Comparison .................... 22
4.4 Result .................................. 23
4.5 Analysis ................................ 24
Chapter 5 Conclusion 27
References 29
List of Figures
1.1 Example of a system with multivariate time series anomaly. . . . . . . . 2
2.1 An example of univariate anomaly detection. . . . . . . . . . . . . . . . 7
2.2 A simple illustration of autoencoder . . . . . . . . . . . . . . . . . . . . 8
2.3 A simple structure of GAN . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.1 A module of LSTM cell . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.2 First stage with autoencoder . . . . . . . . . . . . . . . . . . . . . . . . 14
3.3 Second stage with autoencoder and GAN . . . . . . . . . . . . . . . . . 16
4.1 SWaT testbed process overview.[13] . . . . . . . . . . . . . . . . . . . . 20
4.2 Graphical representation of BATADAL C-Town water distribution system.[19] . . . . . . . . . . . . . 21
4.3 Visualization result on SWaT dataset of different methods . . . . . . . . . 25
4.4 Visualization result on BATADAL dataset of different methods . . . . . . 26
4.5 Visualization result on Rare Event dataset of different methods . . . . . . 26
List of Tables
4.1 Hyperparameters of different methods . . . . . . . . . . . . . . . . . . . 23
4.2 f1 score on SWaT and BATADAL dataset . . . . . . . . . . . . . . . . . 23
4.3 f1 score on Rare Event classification dataset . . . . . . . . . . . . . . . . 24
4.4 statistics for the datasets . . . . . . . . . . . . . . . . . . . . . . . . . . 24
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